Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
The lengthy specification has not been checked to the extent necessary to
determine the presence of all possible minor errors. Applicant's cooperation is
requested in correcting any errors of which applicant may become aware in the
specification.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Claim 1: simulation engine, reinforcement learning agent
Claim 3: environment setting unit, simulation unit
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: Fig 2-4, page 7-8.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. These claims are directed to an abstract idea without significantly more.
As to claim 1,
Step 1: Claim 1 is directed to an apparatus. Therefore, the claim is eligible under Step 1 for being directed to machine.
Step 2A Prong One
Claim 1 recites
a simulation engine (210) configured to set a customized reinforcement learning environment by analyzing, based on design data including entire object information, an individual object and location information of the object, and adding a color, a constraint, and location change information to the analyzed object for each object based on setting information input from a user terminal (UT) (100), (mental process and generic computer function for input)
to perform reinforcement learning based on the customized reinforcement learning environment, to provide state information of the customized reinforcement learning environment and reward information associated with a simulated disposition of a target object as a feedback to a decision made by a reinforcement learning agent (220), (mere instructions to apply an exception)
wherein simulation is performed based on an action determined so that the disposition of the target object around at least one individual object is optimized; (mental process)
and the reinforcement learning agent (220) configured to determine an action so that a disposition of a target object to be disposed around the object is optimized by performing reinforcement learning based on the state information and the reward information provided from the simulation engine (210). (mental process)
The claimed concept is a method of optimizing the disposition of an object by evaluating information based on mathematic relationship directed to “Mental Process”” grouping. These limitations can be performed in a human mind or using pen and paper.
Therefore, claim 1 is an abstract idea.
Step 2A Prong Two
The collecting data step is recited at a high level of generality (i.e., as a general means of collecting input for use in the evaluation step) and amounts to mere data collecting, which is a form of insignificant extra-solution activity.
Recitations of “to perform reinforcement learning based on the customized reinforcement learning environment, to provide state information of the customized reinforcement learning environment and reward information associated with a simulated disposition of a target object as a feedback to a decision made by a reinforcement learning agent (220)” amounts to mere instructions to apply an exception in accordance with MPEP 2106.05(f) (1) and (3). For example, the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. Therefore, claim 1 is an abstract idea.
The claim recites additional elements such as “apparatus, engine and agent”. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component (e.g. executing a software module). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See applicant’s specification page 7-8 and Fig. 2-4 for generic computer description.
The judicial exception is not integrated into a practical application.
Step 2B:
The same analysis of Step 2A Prong Two applies here in 2B. The present claim does not recite any limitation that would integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. See MPEP 2106.05(d).
Thus, claim 1 is not patent eligible. Same conclusion for dependent claims of claim 1. See below.
2. The apparatus of claim 1, wherein the design data is semiconductor design data including CAD data or netlist data. (data description)
3. The apparatus of claim 1, wherein the simulation engine (210) comprises:
an environment setting unit (211) configured to set a customized reinforcement learning environment by adding a color, a constraint, and location change information for each object based on setting information input from the UT (100); (mental process)
a reinforcement learning environment configuration unit (212) configured to produce simulation data for configuring a customized reinforcement learning environment by analyzing, based on the design data including the entire object information, an individual object and location information of the object, and adding a color, a constraint, and location change information which is set by the environment setting unit (211) for each individual object, and to request, from the reinforcement learning agent (220) based on the simulation data, optimization information for a disposition of a target object around at least one individual object; (mental process)
and a simulation unit (213) configured to perform simulation that configures a reinforcement learning environment associated with a disposition of a target object based on an action received from the reinforcement agent (220), and to provide state information that includes disposition information of a target object to be used for reinforcement learning and reward information to the reinforcement learning agent (220). (mere instructions to apply an exception)
4. The apparatus of claim 3, wherein the reward information is calculated based on a distance between an object and a target object or the location of the target object. (mental process)
Same conclusion for independent claim 5 and dependent claims. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In particular, the claim limitations do not recite a combination of additional elements that tie or “integrate the invention into a practical application”.
Thus, claims 1-6 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-6 is/are rejected under 35 U.S.C. 103 as obvious over Mirhoseini et al (NPL: A graph placement methodology for fast chip design, 2021), hereinafter Mirhoseini.
1. A user learning environment-based reinforcement learning apparatus, the apparatus comprising:
Mirhoseini: (page 210)
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Mirhoseini discloses a simulation engine (210) configured to set a customized reinforcement learning environment by analyzing, based on design data including entire object information, an individual object and location information of the object,
Mirhoseini: (page 213)
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See page 208 for more additional detail. Examiner interpreted “entire object’ as “chip layout” and “individual object” as “macros, cell or wire”.
Mirhoseini discloses adding a color, a constraint, and location change information to the analyzed object for each object based on setting information input from a user terminal (UT) (100), to perform reinforcement learning based on the customized reinforcement learning environment, to provide state information of the customized reinforcement learning environment and reward information associated with a simulated disposition of a target object as a feedback to a decision made by a reinforcement learning agent (220),
Mirhoseini: (page 213-214) Detail methodology
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Mirhoseini: (page 215)
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See Fig. 1 and Extended Data Fig. 4 for additional detail.
Examiner considers the “user terminal” correspond to “input/output of a computer system”.
It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify the setting of Mirhoseini to include the color setting since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable dimension involves only routine skill in the art. See MPEP 2144.04 IV. In re Dailey, 357 F.2d 669, 149 USPQ 47 (CCPA 1966).
Mirhoseini discloses wherein simulation is performed based on an action determined so that the disposition of the target object around at least one individual object is optimized;
Mirhoseini: (page 214)
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Examiner considers “training” correspond to “simulation”
Mirhoseini discloses the reinforcement learning agent (220) configured to determine an action so that a disposition of a target object to be disposed around the object is optimized by performing reinforcement learning based on the state information and the reward information provided from the simulation engine (210).
Mirhoseini: (page 208)
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2. The apparatus of claim 1, Mirhoseini discloses wherein the design data is semiconductor design data including CAD data or netlist data.
Mirhoseini: (page 208)
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3. The apparatus of claim 1, Mirhoseini discloses wherein the simulation engine (210) comprises: an environment setting unit (211) configured to set a customized reinforcement learning environment by adding a color, a constraint, and location change information for each object based on setting information input from the UT (100);
Mirhoseini: (page 223)
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See page 209 for additional detail for setting.
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Mirhoseini discloses a reinforcement learning environment configuration unit (212) configured to produce simulation data for configuring a customized reinforcement learning environment by analyzing, based on the design data including the entire object information, an individual object and location information of the object, and adding a color, a constraint, and location change information which is set by the environment setting unit (211) for each individual object,
Mirhoseini: (page 213)
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See page 208 for more additional detail. Examiner interpreted “entire object’ as “chip layout” and “individual object” as “macros, cell or wire”.
Mirhoseini discloses to request, from the reinforcement learning agent (220) based on the simulation data, optimization information for a disposition of a target object around at least one individual object; and
Mirhoseini: (page 211)
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Mirhoseini discloses a simulation unit (213) configured to perform simulation that configures a reinforcement learning environment associated with a disposition of a target object based on an action received from the reinforcement agent (220), and to provide state information that includes disposition information of a target object to be used for reinforcement learning and reward information to the reinforcement learning agent (220).
Mirhoseini: (page 213-214) Detail methodology
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Mirhoseini: (page 215)
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See Fig. 1 and Extended Data Fig. 4 for additional detail.
4. The apparatus of claim 3, Mirhoseini discloses wherein the reward information is calculated based on a distance between an object and a target object or the location of the target object.
Mirhoseini: (page 214)
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Regarding Claim 5, the same ground of rejection is made as discussed above for substantially similar rationale of claim 1 and 4.
Regarding Claim 6, the same ground of rejection is made as discussed above for substantially similar rationale of claim 2.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHUEN-MEEI GAN whose telephone number is (469)295-9127. The examiner can normally be reached Monday-Friday 9:00 am to 4:00 pm EST.
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/CHUEN-MEEI GAN/Primary Examiner, Art Unit 2189